Porcine endogenous retroviruses (PERV) can infect human cell in vitro, which raised widely concerns re-garding the transmission of PERV to xenograft recipients. It's essential to establish a method for detection of PERV.3 pairs of primers were synthesized according to the sequence of gag, pol and env gene of PERV. Polymerase chainreaction (PCR) and reverse transcription PCR (RT-PCR) assays were performed for detection of PERV provirusDNA and PERV specific mRNA. The results showed that provirus DNA and mRNA of PERV existed and expressedin all 4 tested cell lines. The sizes of amplified fragments are identical with the predicted. These methods may be suit-able for monitoring PERV in other cells or tissue.

Objective To evaluate the value of multi-slice spiral computed tomography angiography(MSCTA)scanning and reconstruction technology in detecting anomalous origin of coronary artery(AOCA).Methods Retrospective analysis was done in 3 856 patients who accepted MSCTA.Volume rendering(VR),multi-planar reformation(MPR),curved planar reformation(CPR)and maximum intensity projection(MIP)were used to observe the origin and course of coronary artery.Results 42 patients with AOCA were detected among 3 856 objects,and the detection rate was 1.09%.The detection rates had no statistically significant difference between male(1.17%)and female(0.98%).The rate of patients with anomalous origin of left coronary artery was 30.95%(13/42), and 9 objects(69.23%,9/13)of them had the anomalous origin of left circumflex.The rate of patients with anomalous origin of right coronary was 66.67%(28/42),and 35.71% of them(17/28)were found to have the anomalous origin of right coronary artery from the left sinus of valsalva.Conclusion MSCTA scanning and reconstruction technology is noninvasive,rapid,accurate and intuitive.

Objective:To explore the association between glutamyl transpeptidase (GGT) trajectories and new-onset metabolic syndrome to provide insights for the prevention and treatment of metabolic syndrome.Methods:A total of 3 209 subjects who met the inclusion criteria were enrolled in the study cohort of physical examination population. The GGT levels before follow-up were classified by R LCTMtools program into 3 GGT trajectory groups: low-stable group, medium-stable group and high-stable group. Cox proportional hazards regression model was used to analyze the correlation between different GGT trajectories and new-onset metabolic syndrome.Results:At the end of follow-up in 2020, the cumulative incidence of metabolic syndrome was 7.0%, and the incidence of metabolic syndrome in the low-stable group, medium-stable group and high-stable group were 3.9%, 11.4%, and 15.0%, respectively, showing a growth trend ( P<0.001). After adjusting for multiple confounding factors by Cox proportional hazards regression model, the risk of metabolic syndrome in medium-stable group and high-stable group increased in the total population. The hazard ratios (95% CI)for the high stable group in males and the medium-stable group in females were 1.67(1.07-2.60) and 3.29(1.14-9.53), respectively, compared with their respective low-stable group. Conclusion:Elevated longitudinal trajectory of GGT is a risk factor for new-onset metabolic syndrome, the risk of metabolic syndrome in the total population increased with the increase of long-term GGT level. It is recommended to maintain the long-term level of GGT at about 28 U/L in males and 14 U/L in females, respectively, to achieve the goal of early prevention of metabolic syndrome.

In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.

Objective:To construct enhanced computed tomography (CT)-based nomograph model, to assist physicians in differentiating gastric schwannoma from gastric stromal tumor.Methods:From January 1, 2012 to January 1, 2022, at the Second Affiliated Hospital of Zhejiang University School of Medicine and Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, 57 patients with gastric schwannoma and 275 patients with gastric stromal tumor confirmed by surgical pathology were retrospectively collected, among whom 39 patients with gastric schwannoma and 201 patients with gastric stromal tumor were enrolled in the training set, and the other 18 patients with gastric schwannoma and 74 patients with gastric stromal tumor were enrolled in the validation set. The contrast-enhanced CT imaging features (tumor size index, arterial phase CT value, venous phase CT value, necrosis, calcification, integrity of mucosal surface, and uniform enhancement, etc.) and clinical data (history of gastritis, carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen, and monocyte to lymphocyte ratio (MLR), etc.) were collected. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen the independent predictive factors of imaging features in the differential diagnosis of gastric schwannoma and gastric stromal tumor, and a nomograph model was constracted. Logistic regression analysis was used to analyze and screen the independent predictive factors of clinical indicators to distinguish gastric schwannoma from gastric stromal tumor, and a clinical control model was established. The receiver operating characteristic curve(ROC) was used to analyze the area under the curve (AUC) of the nomograph model in the training set and the verification set, and concordance index (CI) and decision curve analysis (DCA) were used to evaluate the predictive efficiency and clinical application value of the nomograph model. DeLong test was used for statistical analysis.Results:The results of LASSO regression analysis showed that tumor size index, arterial phase CT value, venous phase CT value, necrosis, calcification, integrity of mucosal surface, and uniform enhancement were independent predictive factors of imaging features in the differential diagnosis of gastric schwannoma and gastric stromal tumor(all P<0.05). The results of logistic regression analysis indicated that the history of gastritis ( OR=0.280, 95% confidence interval 0.138 to 0.566), CA19-9 ( OR=0.940, 95% confidence interval 0.890 to 0.993), carcinoembryonic antigen ( OR=0.794, 95% confidence interval 0.661 to 0.952), and MLR ( OR=0.087, 95% confidence interval 0.009 to 0.860) were independent predictive factors of clinical indicators in the differential diagnosis of gastric schwannoma and gastric stromal tumor ( P<0.001, =0.028, 0.013 and 0.037). The AUCs of the nomograph model in the training and validation set were 0.881 and 0.850, respectively, and the AUCs of the clinical control model in the training and validation set were 0.814 and 0.772, respectively, and the differences were statistically significant ( Z=2.57 and 1.96, P=0.005 and 0.030). The average CI of the nomograph model was 0.885. The results of DCA analysis showed that the overall benefit of the nomograph model was higher than that of the clinical control model. Conclusion:The enhanced CT-based nomograph model can effectively distinguish gastric schwannoma from gastric stromal tumor, and can help physicians to make precise clinical decisions.

Objective:To investigate the correlation between fasting plasma glucose (FPG) and new-onset carotid plaque through latent class trajectory models.Methods:A total of 953 observation objects came from the first affiliated hospital of Zhengzhou University in accordance with the inclusion criteria. According to the FPG values of the observed subjects during the annual physical examination from January 2017 to December 2019, the following four different FPG trajectories groups were determined by latent class trajectory modelling tools: the low-stable group, the medium stable group, the medium-high stable group, and the high stable group. Carotid plaque incidence in each group was followed up in 2020 to compare the differences of the cumulative incidences of the four groups. The Cox proportional risk regression model was used to analyze the correlation between different FPG trajectories and new-onset carotid plaque.Results:The incidence of carotid plaque increased with the increase of FPG trajectories by 11.13%, 19.70%, 23.44%, 23.81%, respectively, with significance ( P<0.001). After adjusting gender, age, BMI and other confounding factors with the cox proportional risk regression model, the risk of carotid plaque in the FPG medium stable group, medium and high stable group, high-stable group was still 1.895 (95% CI: 1.296-2.769), 2.273 (95% CI: 1.241-4.161), 2.527 (95% CI: 1.219-5.241) times of the low stable group (all P<0.05). Conclusion:The long-term high FPG levels are independent risk factors for the incidence of carotid plaque, and controlling FPG at a low level steadily can reduce the risk of carotid plaque.

Objective:To explore the relationship between the long-term dynamic change in alanine aminotransferase(ALT) level and metabolic associated fatty liver disease(MAFLD).Methods:A retrospective study was conducted on 6 864 subjects who underwent four consecutive physical examinations from 2017 to 2020 in a cohort study of physical examination population in Henan Province. The relation between ALT level and the shift of MAFLD risk was analyzed using a multi-state Markov model, and the bidirectional relationship between ALT level and MAFLD was explored using a random intercept cross-lagged model.Results:Multi-state Markov model after adjusting for confounding factors showed that the risk of MAFLD in ALT Q2, Q3, Q4 group was gradually higher than that in Q1 group; Compared with health status, non-alcoholic fatty liver disease and MAFLD status gradually increased the risk of ALT shifting from normal to abnormal. The random intercept cross-lagged model after adjusting for confounding factors showed that there was a significant positive bidirectional relationship between MAFLD and ALT level. The cross-lag effect of MAFLD→ALT level was 0.083(95% CI 0.078-0.087), and the cross-lag effect of ALT→MAFLD was 0.044(95% CI 0.039-0.050). And with the extension of time, the cross-lag effect gradually decreased. Conclusions:There is a significant bidirectional relationship between the long-term dynamic change of ALT level and MAFLD. The occurrence of MAFLD is more likely to increase the risk of elevated ALT level, emphasizing the need for enhanced early prevention and treatment of MAFLD.

Objective:To explore the potential correlation between cumulative pulse pressure (cumPP) level and metabolic syndrome (MetS), and to provide insights for MetS management.Methods:A total of 3 968 subjects who underwent health checkup were selected to form a research cohort, and the data were categorized into three groups based on the tertiles of cumPP levels. Cox proportional hazards regression model was employed to analyze the association between different cumPP levels and the incidence of new-onset MetS. Results:The risk of MetS increased with the increased tiers of the cumPP levels (2.5%, 4.3%, and 4.6%, Ptrend<0.001) during the median follow-up period of 2.16 years. Spearman rank correlation analysis showed that cumPP was positively correlated with waist circumference, systolic blood pressure, diastolic blood pressure and fasting plasma glucose (all P<0.05). The Cox proportional hazards regression adjusted model showed that the risk of MetS in Q2 and Q3 was higher than that in Q1 in the total population, and the same results were observed in males (all P<0.05), while there was no statistical significance in females. Model 3 of the total population adjusted for a variety of confounding factors displayed a higher risk of MetS in Q3 compared with that in Q1[1.654 (95% CI 1.272-2.151) ]. When stratified by sex, and the risk of MetS in Q3 was 1.665 times higher than that in Q1 (95% CI 1.245-2.227), while there was no statistically significant risk in female. According to the visual nomogram of independent risk factors screened by multivariate analysis based on Cox proportional hazards regression model, the incidence of MetS at 1 year, 2 years, and 3 years was 0.18%, 3.97% and 7.39%, respectively. In addition, the dose-response curve was plotted according to cumPP, suggesting that the risk of MetS gradually increased with the increase of cumPP in the total population. Subgroup analyses based on baseline systolic blood pressure levels showed that higher cumPP levels were associated with a higher risk of developing MetS, regardless of whether systolic blood pressure was abnormal. Conclusions:Elevated cumPP levels is significantly related to the incidence of new-onset MetS. Maintaining pulse pressure within an appropriate range over long term is crucial for the management of MetS.

Objective:To investigate the application value of machine learning algorithms for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 277 patients with HCC who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University between May 2015 and December 2018 were collected. There were 235 males and 42 females, aged (56±10)years, with a range from 33 to 80 years. Patients underwent preoperative magnetic resonance imaging examination. According to the random numbers showed in the computer, all the 277 HCC patients were divided into training dataset consisting of 193 and validation dataset consisting of 84, with a ratio of 7∶3. Machine learning algorithms, including logistic regression nomogram, support vector machine (SVM), random forest (RF), artificial neutral network (ANN) and light gradient boosting machine (LightGBM), were used to develop models for preoperative prediction of MVI. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) analysis of risk factors for tumor MVI of the training dataset; (3) construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the paired t test. Count data were described as absolute numbers, and comparison between groups was analyzed using the chi-square test. Univariate and multivariate analyses were performed using the Logistic regression model. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: there were 157 males and 36 females in the training dataset, 78 males and 6 females in the validation dataset, showing a significant difference in the sex between the training dataset and validation dataset ( χ2=6.028, P<0.05). (2) Analysis of risk factors for tumor MVI of the training dataset: of the 193 patients, 108 had positive MVI, and 85 had negative MVI. Results of univariate analysis showed that age, the number of tumors, tumor diameter, satellite lesions, tumor margin, alpha fetaprotein (AFP), alkaline phosphatase (ALP), fibrinogen were related factors for tumor MVI [ odds ratio ( OR)=0.971, 2.449, 1.368, 4.050, 2.956, 4.083, 2.532, 1.996, 95% confidence interval ( CI): 0.943-1.000, 1.169-5.130, 1.180-1.585, 1.316-12.465, 1.310-6.670, 2.214-7.532, 1.016-6.311, 1.323-3.012, P<0.05]. Results of multivariate analysis showed that AFP>20 μg/L, multiple tumors, larger tumor diameter, unsmooth tumor margin were independent risk factors for tumor MVI ( OR=3.680, 3.100, 1.438, 3.628, 95% CI: 1.842-7.351, 1.334-7.203, 1.201-1.721, 1.438-9.150, P<0.05). Larger age was associated with lower risk of preoperative tumor MVI ( OR=0.958, 95% CI: 0.923-0.994, P<0.05). (3) Construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction: ①machine learning algorithm prediction models involving logistic regression nomogram, SVM, RF, ANN and LightGBM were constructed based on results of multivariate analysis including age, AFP, the number of tumors, tumor diameter, tumor margin, and consistency analysis of the logistic regression nomogram prediction model showed a good stability. For the training dataset and validation dataset, the area under curve (AUC) of logistic regression nomogram model, SVM model, RF model, ANN model, LightGBM model was 0.812, 0.794, 0.807, 0.814, 0.810 and 0.784, 0.793, 0.783, 0.803, 0.815, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.731-0.849, 0.744-0.860, 0.752-0.867, 0.747-0.862, Z=0.995, 0.245, 0.130, 0.102, P>0.05) and (95% CI: 0.690-0.873, 0.679-0.865, 0.702-0.882, 0.715-0.891, Z=0.325, 0.026, 0.744, 0.803, P>0.05)]. ② Clinicopathological factors were selected using RF, LightGBM machine learning algorithm to construct corresponding prediction models. According to importance scale of factors to prediction models, factors with importance scale>0.01 were selected to construct RF model, including age, tumor diameter, AFP, white blood cell, platelet, total bilirubin, aspartate transaminase, γ-glutamyl transpeptidase, ALP, and fibrinogen. Factors with importance scale>5.0 were selected to construct LightGBM model, including age, tumor diameter, AFP, white blood cell, ALP, and fibrinogen. Due to lack of factor selection ability, factors based on results of univariate analysis were secected to construct SVM model and ANN model, including age, the number of tumors, tumor diameter, satellite lesions, tumor margin, AFP, ALP, and fibrinogen. For the training dataset and validation dataset, the AUC of SVM model, RF model, ANN model, LightGBM model was 0.803, 0.838, 0.793, 0.847 and 0.810, 0.802, 0.802, 0.836, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.740-0.857, 0.779-0.887, 0.729-0.848, 0.789-0.895, Z=0.421, 0.119, 0.689, 1.517, P>0.05) and (95% CI: 0.710-0.888, 0.700-0.881, 0.701-0.881, 0.740-0.908, Z=0.856, 0.458, 0.532, 1.306, P>0.05)]. Conclusion:Machine learning algorithms can predict MVI of HCC preoperatively, but its application value needs to be further verified by large sample data from multi centers.

Adult ; Arrhythmias, Cardiac ; diagnosis ; physiopathology ; Brugada Syndrome ; Cardiac Conduction System Disease ; Electrocardiography ; Female ; Heart Conduction System ; abnormalities ; physiopathology ; Humans ; Young Adult